We are not going to teach you how to make a 3D visualisation in R because even though at a low density a 3D barchart might work for data with a low density, the alternatives visualisations work in all instances. Another reason, is that there is only one library to make a 3D visualisation and it does not produce a high quality visualisation. If you do want to make a 3D column chart, excel would be a better choice. So lets take a look at that 3D visualisation again and try to come up with alternatives. 

cloud(as.numeric(as.character(Observation))~as.factor(Type_1)+as.factor(Type_2), Dataset, panel.3d.cloud=panel.3dbars,
          xbase=0.3,  
          ybase=0.3, 
          zlab = NULL, 
          col.facet=c("blue", "yellow", "purple", "orange", "green"),
          group = Type_1, 
          scales=list(arrows=FALSE, col=1), xlab = NULL, ylab = NULL, main = NULL,
          par.box = list(col = NA), lcol=NULL
          )

So lets take a quick look at the data. Initially, we will explore fake densely generated data and then we will explore a real dataset. The fake data was generated based on real data exploring number of observations based on number of genes and number of cell types.

###So what are the different alternatives to a 3D column chart?

1. Dodge Bar Chart

ggplot(Dataset, aes(x = Type_1, y = Observation, fill = Type_2)) + geom_bar(position = "dodge", stat = "identity" ) + scale_fill_brewer(palette = "Set3")

2. Faceted bar charts

ggplot(Dataset, aes(x = Type_1, y = Observation)) + geom_bar(position = "dodge", stat = "identity", fill="#FF9999", colour="black" )  + facet_grid(rows = vars(Type_2))

3. Line Chart

ggplot(Dataset, aes(x = Type_1, y = Observation, col = Type_2, group= Type_2 )) +  geom_line() + scale_fill_brewer(palette = "Set3")

4. Scatterplots 

ggplot(Dataset, aes(x = Type_1, y =Observation , col = Type_2 )) +  geom_point() + scale_fill_brewer(palette = "Set3")
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